The cost-effectiveness of palivizumab in infants with cystic fibrosis in the Canadian setting: A decision analysis model
Bibliographic record
Abstract
BACKGROUND: Children with cystic fibrosis (CF) are at higher risk of severe respiratory syncytial virus (RSV) infection, which can lead to a decline in lung function. A monoclonal antibody, palivizumab (PMB), effectively prevents RSV hospitalizations; however, the high cost of PMB, approximately C$10,000 per patient per RSV season, limits its widespread use. We assess the cost-effectiveness of PMB prophylaxis in CF children less than 2 y of age from the Canadian healthcare payer's perspective. METHODS: In 2014, a Markov cohort model of CF disease and infant RSV infections in the Canadian setting was developed based on literature data. Infants were treated with monthly PMB injections over the 5-month RSV season. Lifetime health outcomes, quality-adjusted life years (QALYs) and 2013 $CAD costs, discounted at 5%, were estimated. Findings are summarized as incremental cost-effectiveness ratios (ICERs) and budget impact. Deterministic sensitivity analysis was conducted to assess parameter uncertainty. RESULTS: Implementation of a hypothetical Canadian RSV prophylaxis program resulted in ICERs of C$652,560 (all CF infants) and C$157,332 (high-risk CF infants) per QALY gained and an annual budget impact of C$1,400,000 (all CF infants) and C$285,000 (high-risk CF infants). The analysis was highly sensitive to the probability of severe RSV, the degree of lung deterioration following infection, and the cost of PMB. CONCLUSIONS: Our results suggest PMB is not cost-effective in Canada by commonly used thresholds. However, given the rarity of CF and relatively small budget impact, consideration may be given for the selective use of PMB for immunoprophylaxis of RSV in high-risk CF infants on a case-by-case scenario basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".